How to Train AI Models on a Budget

Really nice, beginner friendly, 7 step guide to fine-tuning LLMs from Unsloth! My simple breakdown 👇 🚀 Getting Started: The 7-Step Process 1️⃣ Choose Your Model & Method For beginners, start with smaller models like Llama 3.1 (8B) and use QLoRA, which combines 4-bit quantization with LoRA to handle large models with minimal resources. This approach uses up to 4× less memory than standard methods! 2️⃣ Prepare Your Dataset Quality matters more than quantity! Structure your data as question-answer pairs for best results. While simply dumping code data can work for certain applications, well-structured datasets generally lead to better performance. 3️⃣ Optimize Your Hyperparameters The guide offers practical ranges for crucial settings: >> Learning rate: 1e-4 to 5e-5 (balance between learning speed and stability) >> Epochs: 1-3 (more than 3 reduces creativity but may decrease hallucinations) >> Context length: Start with 2048 tokens for testing 4️⃣ Avoid Common Pitfalls >> Overfitting: When your model memorizes training data instead of learning to generalize Solutions: Reduce learning rate, fewer epochs, combine with generic datasets >> Underfitting: When your model doesn't learn enough from training Solutions: Increase learning rate, more epochs, more relevant data 5️⃣ Training During training, aim for a loss value close to 0.5. The guide recommends: >> per_device_train_batch_size = 2 >> gradient_accumulation_steps = 4 >> max_steps = 60 (or num_train_epochs = 1 for full runs) >> learning_rate = 2e-4 6️⃣ Evaluation For evaluation, you can either: >> vibe check: Chat with the model to assess quality manually >> test check: Set aside 20% of your data for testing >> Use automatic evaluation tools like EleutherAI's lm-evaluation-harness 7️⃣ Save & Deploy The fine-tuned model can be saved as a small 100MB LoRA adapter file or pushed directly to Hugging Face. From there, you can run it using various inference engines like Ollama, vLLM, or Together via the LoRA inference feature. 💡 Why This Matters Fine-tuning lets you create specialized AI agents that can: >> Update domain knowledge without retraining from scratch >> Match your desired tone and communication style >> Optimize for specific tasks like sentiment analysis, customer service, or legal work >> The most exciting part? Fine-tuning can replicate all of RAG's capabilities, but RAG can't replicate all of fine-tuning's benefits. https://lnkd.in/ggWkFMMp

Most are sleeping on the power of 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, and every company should have a Distillation Factory to stay competitive This technique is reshaping how companies build efficient, scalable, and cost-effective AI. First, 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? Also known as knowledge distillation, is a machine learning technique where a smaller, more efficient "student" model is trained to replicate the behavior and performance of a larger, more complex "teacher" model. Think of it as a master chef (the teacher) passing down their culinary expertise to an apprentice (the student) without sharing the exact recipe. The student learns by observing the teacher’s outputs and mimicking their decision-making process, resulting in a lightweight model that retains much of the teacher’s capabilities but requires fewer resources. Introduced by Geoffrey Hinton in his 2015 paper, “Distilling the Knowledge in a Neural Network,” the process involves: 1/ Teacher Model: A large, powerful model trained on massive datasets. 2/ Student Model: A smaller, efficient model built for faster, cheaper deployment. 3/ Knowledge Transfer: The student learns from the teacher’s outputs—distilling its intelligence into a lighter version. There are several types of distillation: 1/ Response-Based: The student mimics the teacher’s final outputs 2/ Feature-Based: The student learns from the teacher’s intermediate layer representations. 3/ Relation-Based: The student captures relationships between the teacher’s outputs or features. The result? A student model that’s faster, cheaper to run, and nearly as accurate as the teacher, making it ideal for real-world applications. 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝘁𝗼𝗿𝘆? In today’s AI landscape, very large LLMs are incredibly powerful but come with significant drawbacks: high computational costs, massive energy consumption, and complex deployment requirements. A Distillation Factory is a dedicated process or team focused on creating distilled models, addressing these challenges and unlocking transformative benefits. Here’s why every company should invest in one: 1/ Cost Efficiency: Distilled models cut costs, running on minimal GPUs or smartphones, not data centers. 2/ Scalability: Smaller models deploy easily. 3/ Faster Inference: Quick responses suit real-time apps. 4/ Customization: Tailor models for healthcare or finance with proprietary data, no full retraining. 5/ Sustainability: Lower compute needs reduce carbon footprints, aligning with green goals. 6/ Competitive Edge: Rapid AI deployment via distillation outpaces costly proprietary models. A Distillation Factory isn’t just a technical process; it’s a strategic move.

𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐥𝐢𝐦𝐢𝐭𝐬 𝐢𝐬𝐧’𝐭 𝐚 𝐜𝐮𝐫𝐬𝐞. 𝐈𝐭’𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐭𝐞𝐚𝐜𝐡𝐞𝐫. Training an 8GB dataset of the LEGO Group on a CPU in free Google Colab? Not ideal. But possible. 𝐀𝐟𝐭𝐞𝐫 𝐦𝐚𝐧𝐲 𝐭𝐫𝐢𝐞𝐬, 𝐈 𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐟𝐨𝐫 20 𝐞𝐩𝐨𝐜𝐡𝐬 𝐰𝐢𝐭𝐡 500 𝐢𝐦𝐚𝐠𝐞𝐬. It worked kind of. The model showed good examples but struggled with new images. Lessons learned: ↳ 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐥𝐢𝐦𝐢𝐭𝐬 𝐬𝐩𝐚𝐫𝐤 𝐜𝐫𝐞𝐚𝐭𝐢𝐯𝐢𝐭𝐲: Constraints forced smarter data handling and model tweaks. ↳ 𝐒𝐦𝐚𝐥𝐥 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 𝐫𝐞𝐯𝐞𝐚𝐥 𝐛𝐢𝐠 𝐠𝐚𝐩𝐬: 500 images can highlight model weaknesses quickly. ↳ 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Good performance on one batch isn't enough. ↳ 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐚𝐭𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧: Every failed run taught something new. Now, you might be asking can you continuing from the last checkpoint, trained with a new batch of 500 images. 𝐓𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐢𝐧 𝐚𝐫𝐞𝐚𝐬 𝐢𝐭 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞, 𝐛𝐮𝐭 𝐧𝐞𝐰 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐩𝐩𝐞𝐚𝐫𝐞𝐝. 𝘒𝘦𝘺 𝘵𝘢𝘬𝘦𝘢𝘸𝘢𝘺𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘴𝘦𝘤𝘰𝘯𝘥 𝘳𝘰𝘶𝘯𝘥: ↳ 𝐂𝐡𝐞𝐜𝐤𝐩𝐨𝐢𝐧𝐭𝐢𝐧𝐠 𝐬𝐚𝐯𝐞𝐬 𝐭𝐢𝐦𝐞: Resuming training avoids starting from scratch. ↳ 𝐃𝐢𝐯𝐞𝐫𝐬𝐞 𝐝𝐚𝐭𝐚 𝐛𝐨𝐨𝐬𝐭𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: New images exposed different learning opportunities. ↳ 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥: Fine-tuning hyperparameters helped the model adapt. ↳ 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Regular evaluation keeps improvements on track. Perfect models are rare. Progress comes from testing, learning, and trying again. ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation along with my journey. #AIInnovation #MachineLearning #DataScience #TechLeadership #AIDevelopment

Based on both the AI Index Report 2025 and the Securing AI Agents with Information-Flow Control (FIDES) paper, here are actionable points tailored for organizations, and AI teams, Action Points for AI/ML Teams 1. Build Secure Agents with IFC Leverage frameworks like FIDES to track and restrict data propagation via label-based planning. Use quarantined LLMs + constrained decoding to minimize risk while extracting task-critical information from untrusted sources. 2. Optimize Cost and Efficiency Use smaller performant models like Microsoft’s Phi-3-mini to reduce inference costs (up to 280x lower than GPT-3.5). Track model inference cost per task, not just throughput—consider switching to open-weight models where viable. 3. Monitor Environmental Footprint Measure compute and power usage per training run. GPT-4 training emitted ~5,184 tons CO₂; Llama 3.1 reached 8,930 tons. Consider energy-efficient hardware (e.g., NVIDIA B100 GPUs) and low-carbon data centers. #agenticai #responsibleai